Modified Artificial Bee Colony Algorithm with Multilevel Threshold Segmentation and Boundaries Evaluation for Shadow Detection
Madhu Shandilya1, Rakesh Kumar Das2
1Madhu Shandilya, Professor in electronics and communication engineering department, Maulana Azad National Institute of Technology, Bhopal, India.
2Rakesh Kumar Das, received the BE degree in Electronics and communication engineering from RGPV University and the M.Tech in digital communication from MANIT Bhopal, India.
Manuscript received on 27 August 2019. | Revised Manuscript received on 12 September 2019. | Manuscript published on 30 September 2019. | PP: 1395-1399 | Volume-8 Issue-11, September 2019. | Retrieval Number: J97430881019/2019©BEIESP | DOI: 10.35940/ijitee.J9743.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The appearance of shadows typically causes severe problems in pc vision. There are various methods have already put forward but scope in this field is open. In this article Shadow Detection and Removal Using Modified artificial bee colony (MABC) Algorithm with Multilevel Threshold segmentation is proposed. The proposed method uses three threshold and corresponding boundaries, associated curvature, edge response, gradient, and MABC algorithm. First data pre-processing is applied to find the correlation between the pixels then three threshold and corresponding boundaries evaluated to accurately differentiate pixels as foreground. The edge response, curvature, gradient are applied to find the boundaries. Finally, MABC has been applied for detecting the shadow. The results show improvement in comparison with other existing methods.
Keywords: Shadow detection, Artificial bee colony, Curvature, Gradient, Edge response, Boundary value.
Scope of the Article: